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sustainability. Swine production, a key economic pillar in Quebec, Canada (CAD $1.6B in production revenues and $4.4B in processing), faces urgent challenges: rapid disease detection, animal welfare monitoring
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This PhD at Cranfield University explores the development of resilient, AI-enabled electronic systems capable of detecting faults and autonomously recovering from failures in real time. The project
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impacts on animal performance. Early detection of these diseases remains a challenge, as it still relies heavily on human observation, which is time-consuming and prone to error. Technologies such as
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an adhesive heavy application, and to find methods for End-of-Life disassembly. Battery pack construction uses adhesives to fix multiple cells in place, creating a block of battery modules. The adhesive has
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downtime and operational costs. Traditional condition monitoring approaches often face challenges in accurately detecting early-stage faults, especially in the presence of highly impulsive signals
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Fuel Rig with Five Degradation Faults: Simulates various degradation scenarios in unmanned aerial vehicle (UAV) fuel systems, enabling research into fault detection, isolation, and prognostics. Machine
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control system that enhances Annual Energy Production (AEP), reduces mechanical stress, and improves fault detection using machine learning (ML) and physics-based modelling. The candidate will gain hands
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suite of specialised facilities: UAV Fuel Rig with Five Degradation Faults: Simulates various degradation scenarios in unmanned aerial vehicle (UAV) fuel systems, enabling research into fault detection
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Degradation Faults: Simulates various degradation scenarios in unmanned aerial vehicle (UAV) fuel systems, enabling research into fault detection, isolation, and prognostics. Machine Fault Simulator
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research into fault detection, isolation, and prognostics. Machine Fault Simulator for Rotating Machinery Faults: A versatile platform that replicates common faults in rotating machinery, such as imbalance